Various means and methods are used today for monitoring emissions constituents in the exhaust of an internal combustion engine such as, for example, gasoline, diesel, natural gas, or other types of internal combustion engines, that utilize various emissions after-treatment devices such as an exhaust particulate filter, for example diesel particulate filters (DPF) and gasoline particulate filters (GPF), to reduce particulate matter emissions or various catalysts, traps, and scrubbers to reduce gaseous emissions, such as selective catalytic reduction systems (SCR), NOX traps, hydrocarbon traps, ammonia slip catalysts, oxidation catalysts, three-way catalysts, and the like.
Indirect methods (utilizing predictive models or so-called virtual sensors) have been employed to indirectly estimate engine emissions. These indirect methods have suffered from a number of shortcomings including for example the fact that these models are typically developed and calibrated given a specific set of boundary conditions or system inputs including, but not limited to, engine characteristics and operating parameters, fuel and lubricant type, aging factors, safety margins, and the like that require tuning to a specific set of input conditions which may not be universally applicable to all engines or systems and thus require some customization for each end-use application.
Virtual sensors that rely on these known set of operating conditions to accurately estimate engine emissions such as, for example, the composition, amount, rate, or concentration of the emissions in the exhaust have not by definition functioned appropriately over conditions or abnormal operation outside the capabilities of the predictive models.
Also, changes that occur to the engine as the engine ages and components wear or break down, or changes that occur to the catalysts as the catalysts age or become poisoned, cannot be dynamically captured utilizing predictive models. Generally, safety factors or deterioration factors are used to compensate for these changes resulting in a trade-off in overall performance that is generally too conservative when the engine is new in order to satisfy the system useful life requirements. The predictive model approach also suffers from the lack of any feedback mechanism to directly determine whether the system performance has degraded beyond the assumed safety margins. Moreover, the development, calibration, and tuning of the predictive models for a specific engine and application is time consuming and costly.
In the case of particulate filters, pressure or differential pressure sensors have also been used but they suffer from a lack of resolution and response. In particular, exhaust backpressure or measurements of the particulate filter differential pressure are impacted by a wide range of noise factors including exhaust flow rate, temperature, particulate matter distribution, filter characteristics (hysteresis effects), and the like. Pressure measurements also do not provide a direct measure of particulate matter in the exhaust and lack the resolution to detect particulate matter build-up on the filter necessary to estimate engine-out PM emissions rates. Furthermore, pressure measurements are not reliable overall operating conditions, such as low flow (idle), with the engine off, during regeneration, or over transient events for example. This approach, therefore, does not provide a continuous measurement.
The use of pressure sensors also typically requires significant averaging or filtering to reduce the noise effects on the measurements. This signal averaging or filtering significantly increases the sensor's response time, making it unsuitable for any type of meaningful feedback control applications.
Soot sensors have also been used to measure the concentration of soot particles in the exhaust. Soot sensors however have a low measurement range thus resulting in a sensor that is quickly overwhelmed by the high levels of engine-out soot emissions. Also, soot sensors are designed to measure very low concentrations of soot in the exhaust gas stream (after the particulate filter) and are not suitable for measuring high levels of engine-out particulate matter emissions. Further, soot sensors only monitor a portion of the exhaust gas flow, and therefore do not provide a direct measurement of the total soot levels in the exhaust gas, but only the levels in the exhaust gas in close proximity to the sensor (or flowing through the sensor housing). Soot sensor accuracy is also affected by exhaust flow velocity, location of the sensing element in the exhaust pipe (as it only samples a small volume of the flow), temperature, particle morphology and composition, and accumulation of deposits (ash, catalyst/washcoat particles) as the sensor ages.
Accumulation type soot sensors have also been used. These sensors however do not provide a continuous monitor but rather cycle from a measuring state to a regeneration state. The regeneration state generally requires additional energy input to burn off any accumulated soot on the sensing element. The sensors also require condensate protection, which does not allow them to operate during certain conditions, such as cold start for example when they may be needed most. Accumulation type soot sensors further do not directly monitor the soot particle number or mass in the exhaust stream, but rather the time for a certain amount of material to accumulate on the sensing element, thereby providing only an indirect indication of soot levels in the exhaust. Soot sensors also suffer from poor durability, the accumulation of contaminants (such as ash), as well as thermal shock (water in the exhaust or condensation), which limits the sensor life and accuracy over its useful life. Due to the intermittent nature of the sensor operation which includes regeneration event followed by time period required for sufficient accumulation to generate a measurable response, these sensors also do not provide a continuous measurement.
A number of different types of gas sensors are also used, such as for example, NOx sensors, oxygen sensors, ammonia sensors, and other related sensors, which also suffer from many of the deficiencies described above. Many of these sensors use electrochemical cells to conduct the measurements. These types of sensing elements are fragile and may suffer from a number of failure modes in the field. In particular, it is well known that many gas sensors suffer from cross-sensitivities to other exhaust gas constituents, errors due to variations in the local gas velocity or flow rate near the sensing element, and temperature-related effects, among others. These sensors also sample only a portion of the exhaust flow and not the flow in its entirety. These sensors may also become poisoned due to contaminants in fuels, lubricants, or the environment. In another example, the sensor may become damaged when used in certain conditions. Many of these sensors also require significant energy input, such as from heaters, to enable their operation, and may not function over all operating conditions, such as changes in the air-fuel ratio in one example, or cold start conditions in another.
The present invention provides a direct, accurate, and fast response measurement of engine exhaust constituent levels using radio frequency measurements based on the interactions of the exhaust constituents with the emissions aftertreatment system, and directly addresses the deficiencies noted above.
The present invention further provides for a much simpler and more robust interface to the exhaust system, using only an antenna to transmit or receive the radio frequency signal to remotely probe or monitor the aftertreatment system (filter or catalyst) which itself serves as the sensor.